What is Artificial Intelligence Agent (AI Agent)?

Artificial intelligence agents, also called AI agent, can be used to perform certain tasks by combining artificial intelligence models with tools, APIs, data sources, memory and various rules. In particular, it is possible to use software development, customer service, research, data analysis and enterprise workflows in multiple stages. OpenAI defines agents as systems that can perform tasks independently on behalf of the user, Google Cloud also pay attention to the capabilities of reasoning, vehicle handling and action-realization in line with the target of agents.

What is Artificial Intelligence Agent?

Artificial intelligence agent is a software system that uses artificial intelligence model to realize a goal given to it and performs multiple transactions using external tools when necessary.

The user asks a question in a traditional AI application and produces a response to the system. In artificial intelligence agent, the process can transmit more different. The agent analyzes the task given, determines the information required, selects the appropriate tools, perform operations and decides to the next step according to the results obtained.

OpenAI stands out especially two features to evaluate as a system agent: Access to vehicles to use an artificial intelligence model to perform work flow and manage decisions and interact with external systems.

For example, the artificial intelligence agent used in a company’s customer support system may not only answer the customer’s question. You can review the customer record, check the order information, evaluate the company policy and perform a process within the authority.

Therefore, instead of assessing artificial intelligence agents only as "more advanced chatbots", it becomes more accurate to think as systems that combine artificial intelligence and software tools to perform certain goals.

What is AI Agent?

AI agent is English equivalent of artificial intelligence agent. AI agent systems allow an artificial intelligence model to use the vehicle and perform certain actions by passing beyond only producing content.

For example, an AI agent can search on a database, you can get information from an API, examine files, research on the web, write code and test, create record in a CRM system, help from any other agent when necessary.

However, having an artificial intelligence agent of a system does not necessarily mean it moves completely independently. It depends on how autonomous the agent will be, the author of the developer, the tools used and the design of the system.

How Artificial Intelligence Agent Works?

The working format of artificial intelligence agents can vary according to the architecture used. However, the basic process is often shaped around the understanding of a target, the collection, planning, the use of tools and evaluation of results. A simplified artificial intelligence agent process can be considered as follows:

Goal → Analysis → Planning → Vehicle use → Results Evaluation → New step → Completion of the task

This structure is one of the basic points that agents leave from classic automation systems. Google Cloud This structure is dealt with components such as model, vehicles, memory, orkestration and working environment.

How to Plan Artificial Intelligence Agent Task?

When the user gives a goal to the agent, the system tries to understand the task first.

For example:

"We prepare a report that can analyze the latest three-month sales data of our company and offer management. "

Let’s think that a task is given.

The steps that the agent can perform can be:

1. Identify where the sales data is located.

2. Access the relevant data source.

3. Review data.

4. Make required calculations.

5. Identify unusual changes.

6. Edit findings.

7. Create reports.

All these steps may not be required to identify individually by the user. According to the working format of the agent, the model can dynamically determine the steps necessary to complete the task.

Sample Agent Task

For example a customer:

"Why did I still have not been sent? "

When asked, the agent can perform the following operations:

Find customer information → Identify order number → query the order system → Check the shipping status → Clear the customer

Here artificial intelligence does not only create a text. It works to complete the task by taking information from different systems.

Basic Components of Artificial Intelligence Agents

While the architecture of a AI agent system changes according to the technology used, some basic components are prominent. Microsoft’s agent architecture documentation client, infrastructure, orchestra, language model and vehicle call capabilities are shown among basic architectural components.

Artificial Intelligence Model

The model is located in the center of the decision-making and natural language understanding mechanism of the agent. Large language models can be used to understand user instructions, evaluate current situation, choose the tool and interpret results.

However, there is no need to use the same model in each task. Different models can be preferred according to factors such as complexity of the task, cost and response time. OpenAI also proposes attention to the complexity of the task in selection, the balance between delay and cost.

Tools and APIs

The tools allow the agent to communicate with external systems. Tools for a corporate agent can be connected to systems such as CRM, ERP, database, email, calendar file system, web search in-house information base. The processes that the agent can perform largely depend on the ability that vehicles offer.

Memory

Memory can protect the necessary information between the agent’s task or different interactions. For example, a customer service agent can use the information in the current interview shortly. Specific user or process information can be stored for longer time in more advanced systems.

Orkestration

Orkestrationrefers to how the agent manages different components. Communication between model, vehicles, memory, data sources and other agents can be managed by orkestration layer.

What is the Difference Between Chatbot with Artificial Intelligence Agent?

The main difference between Chatbot and AI agent is that the system does not only produce answers, but it can not perform transactions in accordance with the target.

A agent; User goal → Planning → Information collection → Vehicle use → Action → Performs a long process in the form of result.

For example; "Describe this text." A chatbot can be enough for the request.

However:

"By reviewing this report, identifying important financial changes, compare with relevant data in the company and prepare a report that I can send to my manager. "

As a task may require more tools and multi-step transactions.

How to Use Agent Based Systems?

The use area of agent-based systems can vary according to the tools and authorizations given to the agent. They can be used in the work, which consists of multiple stages and contains unstructured information.

Artificial Intelligence Agents in Software Development

Software development is one of the areas where AI agent uses stand out. A coding agent can examine the code base, find relevant files, make code changes, run tests, analyze errors, apply new changes.

For example, the developer; " Find and correct the verification error on the user record screen." When said, it can determine the files that the agent has the code base, change and run the test. The main difference here is a multi-step task execution on the code, not only the code generation of the agent.

Artificial Intelligence Agents in Customer Services

Customer service is another area where agents can be used. A customer service agent:

1. Can understand customer's demand.

2. You can find customer account.

3. You can review order or transaction history.

4. Can control company policy.

5. Determine the appropriate solution.

6. If there is a authorization, it can perform the process.

7. When necessary, it can transfer to human representative.

This approach is particularly similar to numerous but each can be used in customer demands requiring different context.

AI Agent Use in Research

Research agents can be used to examine different data sources and answer a question. For example:

"In the last period of our opponents announced that the artificial intelligence products are researched and compared the common features. "

As a task, the agent can search for web, examine different resources and make the information obtained into a report. However, the results produced by research agents should be passed through welding control, especially for critical decisions.

Artificial Intelligence Agents in Data Analysis

In data analysis processes, agents can be connected to databases or analysis tools. For example:

"In the last six months, analyze why sales fall. "

When a task is given, the agent may question the necessary data, compare results and form reports. The success of the agent here depends not only on the natural language ability, the quality of data sources, access permissions and accuracy of the analysis performed.

How to Create Artificial Intelligence Agent?

To create an AI agent, firstly it is necessary to clearly identify what the agent will do.

Identify Purpose of Use

At the first stage, which problem will be solved should be determined. For example:

• Manage customer requests

• Invest in internal documents

• Review code

• Prepare reports

• Analyze sales data

A specific usage area can be selected, such as.

OpenAI and Anthropic recommends the evaluation of whether the problem really needs agent before the use of the agent and avoid unnecessary architectural complexity.

Write Instructions for the Agent

The task definition of the agent should be clear. Such instructions help determine the behavior limits of the agent.

Prompt Example:

"You are the customer support agent of our company. Analyze customer demands. Use only the company policies and tools that are given to you. Forecasting on a topic you know. Perform transactions without consent from user or authorized employee in high-impact transactions such as refund. "

Description Tools

The tools required by the agent should be determined. For example, customer service agent; customer call, order inquiry, shipping status inquiry, support record creation tools can be given. It is important that the tools that do not need to use the agent are not added to the system, limiting the authorization area.

Create Test Environment

The agent should be tested directly with different scenarios before being taken into the production environment. For example, for customer service agent; normal customer request, missing information, incorrect customer information, transaction requiring authorization, contradictory data, malicious instruction can be said. Anthropic, It emphasizes the importance of comprehensive tests and appropriate security measures by specifying that agents can lead to increased errors and costs.

Is Single Agent, Multiple Agents?

There is no need for multiple agent architectures of each system.

Single Agent System

An agent in one-piece system tries to complete the task using different tools. For example, a research agent: Search → Resources review → Information comparison → Report preparation can manage itself. Since this structure is simpler, the development and testing process can also be easier.

Multiple Agent System

Multiple agent system Different tasks can be distributed to different agents. For example:

Manager agent

↓

Research agent + Data analysis agent + Reporting agent

A architecture can be created in the form.

Data analysis agent can examine numerical data while collecting research agent resources. Reporting agent can bring results together. Anthropic suggests evaluation of different architectures according to the use scenario from single-to-employed systems to multiple agent orkestration.

Difference Between Agentic Workflow and AI Agent

It is important to separate the flow and agent concepts in agents based systems.

The process in a row is largely pre-defined.

For example:

Text → Abstract → Translate → Translate → Kayde Tags

can be defined by a process sequence code like.

In an agent-based system, the model can decide which step and vehicle will be used more dynamically according to the current state of the task.

Is the Agent Used For Every Work?

Classic automation or for a simple task with specific steps Home can be more convenient. Google Cloud also states that the agencyic approach is not always necessary in predefined tasks such as document abstracting, translation or classification.

Agents more:

• Open-end tasks

• Multi-step processes

• Jobs requiring decision-making

• Working with unified data

• In processes where fixed rules remain insufficient

can be assessed.

Advantages of Artificial Intelligence Agents

The most important advantage of artificial intelligence agents is that productive artificial intelligence can only be used in the realization of certain tasks that are believed to be the content generation tool.

Manage Multi-Step Tasks

Agents can perform a task by separating multiple steps instead of a single process.

Working with vehicles

APIsThe databases and corporate software agent can be included in the working field.

Use Undetected Data

It can be processed on texts, documents, emails and similar data.

Reduction of Human Intervention

Some processes that repeat in properly designed processes can be automated. However, these advantages depend on the implementation of the agent in the correct field of use and reliably testing.

Risks of Artificial Intelligence Agents

Processing of agents in external systems can create different security and control requirements compared to classic chat systems.

Risk to Transaction with Misinformation

Agent Based on misinformation If the tool uses, the error can be moved to the next step.

Therefore, the result verification mechanisms can be used in critical processes.

Authorization Issue

What data a agent can access and which processes can be limited.

NIST focuses on the development of standard and protocols for security, identification and authorization issues, so AI agents can be used safely AI Agent Standards'i launched in 2026.

Prompt Injection Risk

If the agent reads data from external sources, malicious instructions or prompt injection attacks can be found. Therefore, each information received from the outside should not be considered as a reliable instruction, and the data must be separated from the instructions.

Human Approval

For financial transactions, data erasing, critical system changes or other highly effective processes human approval added. This approach can create control mechanism in critical points without completely eliminating otonomi which agent has.

What Should Be Considered When Using Artificial Intelligence Agent?

It is not enough to focus only on the performance of the model when developing or using artificial intelligence agent.

Limit the Authorizations of the Agent

The agent should not be accessed to systems that are not required. For example, an agent that will only read the order status may not have the authority to delete the order.

Use Approval Mechanism in Critical Transactions

Human approval can be added for difficult operations.

Evaluate Outputs

It should be tested not only to answer the agent correctly, but also to complete the task correctly.

Anthropic states that agent assessments can be more complex than classic LLM assessments due to their very step tool usage, status changes and behavior change according to intermediate results.

Transaction Registration Gold

In particular, what means the agent in corporate systems should be tracked when it uses and which processes are performed.

These records may take care of error analysis and system assessment.

The Future of Artificial Intelligence Agents

The development of artificial intelligence agents is not limited to more powerful models. A significant development area where agents can safely communicate with different softwares, data sources and other agents.

The AI Agent Standards Initiative launched in 2026 of NIST focuses on issues such as safe usage, identity, authorization and interoperability among different systems.

Therefore, in the future, AI agent systems are evaluated not only in chat interfaces, but as a expected development area where companies work in more comprehensive systems that work with existing software infrastructures. OpenAI also states that agents expand to the execution of longer-term and multi-step tasks than short interactions.

But it does not mean that a system is more autonomous, it is automatically better. To be suitable for the task of agent architecture, test enough and required security limitsThe creation is necessary.